Keynote 3: Why AI needs even more Data Science, and vice versa

Recent advances in AI and deep learning are capturing headlines, and yet suffer from a variety of short-comings, including catastrophic forgetting, inability to generalize robustly, susceptibility to bias, and inadequate techniques for introspection and explanation. Many of these are challenges where an even greater influence from the expertise and rigorous approaches of data science could have profound effects. For example, AI has an urgent and critical need for learning causal models, an area requiring a sound grasp of statistical analysis, principles of identification and other mainstays of data science. Conversely, differentiable (deep learning) techniques for learning causal structure could bring powerful new tools to data scientists. In another example, information theoretic approaches to understanding information flow in deep neural networks could enable more robust, efficient, and predictable AI. AI for ethical decision making is yet another area with a deep need for complementary data science and AI expertise. This talk will cover these, and other examples of projects we are undertaking in the new MIT-IBM Watson AI Lab, and the necessary interplay of data science and AI. I will also highlight a novel academic+industry approach we are taking to AI research, and why it is both unique and compelling.